DOC: Gather threshold algo in gallery

doc/examples/segmentation/plot_thresholding.py

@@ -4,17 +4,10 @@ Thresholding
 ============
 
 Thresholding is used to create a binary image from a grayscale image [1]_.
-
-Thresholding algorithms can be separated in two categories:
-
-- Histogram-based. The histogram of the pixel intensity is used and
-  assumptions may be made on the properties of this histogram (e.g. bimodal).
-- Local. To process a pixel, only the neighboring pixels are used.
-  These algorithms often require more computation time.
-
-
-Scikit-image includes a function to test thresholding algorithms provided
-in the library. Therefore, in a glance, you can select the best algorithm
+If you are not familiar with the details of the different algorithms and the
+underlying assumptions, it is often to know which algorithm will give the best
+results. Therefore, Scikit-image includes a function to test thresholding algorithms
+provided in the library. At a glance, you can select the best algorithm
 for you data, without a deep understanding of their mechanisms.
 
 .. [1] https://en.wikipedia.org/wiki/Thresholding_%28image_processing%29
@@ -23,10 +16,10 @@ for you data, without a deep understanding of their mechanisms.
 import matplotlib
 import matplotlib.pyplot as plt
 
-from skimage.data import page
+from skimage import data
 from skimage.filters import thresholding
 
-img = page()
+img = data.page()
 
 # Here, we specify a radius for local thresholding algorithm.
 # If it is not specified, only global algorithms are called.
@@ -35,52 +28,35 @@ fig, ax = thresholding.try_all_threshold(img, radius=20,
 plt.show()
 
 """
-
 .. image:: PLOT2RST.current_figure
 
+How to apply a threshold?
+=========================
+
 Now, we illustrate how to apply one of these thresholding algorithms
-This example uses Otsu's method [2]_.
-
-Otsu's method calculates an "optimal" threshold (marked by a red line in the
-histogram below) by maximizing the variance between two classes of pixels,
-which are separated by the threshold. Equivalently, this threshold minimizes
-the intra-class variance.
-
-.. [2] http://en.wikipedia.org/wiki/Otsu's_method
-
+This example uses the mean value of pixel intensities. It is a simple
+and naive threshold value, which is sometimes used as a guess value.
 """
-import matplotlib
-import matplotlib.pyplot as plt
 
-from skimage.data import camera
-from skimage.filters import threshold_otsu
-
-
-matplotlib.rcParams['font.size'] = 9
-
-
-image = camera()
-thresh = threshold_otsu(image)
-binary = image > thresh
-
-fig = plt.figure(figsize=(8, 2.5))
-ax1 = plt.subplot(1, 3, 1, adjustable='box-forced')
-ax2 = plt.subplot(1, 3, 2)
-ax3 = plt.subplot(1, 3, 3, sharex=ax1, sharey=ax1, adjustable='box-forced')
-
-ax1.imshow(image, cmap=plt.cm.gray)
-ax1.set_title('Original')
-ax1.axis('off')
-
-ax2.hist(image)
-ax2.set_title('Histogram')
-ax2.axvline(thresh, color='r')
-
-ax3.imshow(binary, cmap=plt.cm.gray)
-ax3.set_title('Thresholded')
-ax3.axis('off')
-
-plt.show()
+#from skimage.filters.thresholding import threshold_mean
+#from skimage import data
+#image = data.camera()
+#thresh = threshold_mean(image)
+#binary = image > thresh
+#
+#fig, axes = plt.subplots(nrows=2, figsize=(7, 8))
+#ax0, ax1 = axes
+#
+#ax0.imshow(image)
+#ax0.set_title('Original image')
+#
+#ax1.imshow(binary)
+#ax1.set_title('Result')
+#
+#for ax in axes:
+#    ax.axis('off')
+#
+#plt.show()
 
 """
 .. image:: PLOT2RST.current_figure